Overview

Dataset statistics

Number of variables83
Number of observations13734
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 MiB
Average record size in memory664.0 B

Variable types

Numeric14
Categorical69

Warnings

weight_16 is highly correlated with height_16High correlation
height_16 is highly correlated with weight_16High correlation
comp_noint_bed_16 is highly correlated with comp_int_bed_16High correlation
comp_int_bed_16 is highly correlated with comp_noint_bed_16High correlation
text_wend is highly correlated with text_weekHigh correlation
comp_wend is highly correlated with comp_weekHigh correlation
text_week is highly correlated with text_wendHigh correlation
comp_week is highly correlated with comp_wendHigh correlation
has_dep_diag is highly correlated with prim_diagHigh correlation
prim_diag is highly correlated with has_dep_diagHigh correlation
comp_noint_bed_16 is highly correlated with comp_int_bed_16High correlation
comp_int_bed_16 is highly correlated with comp_noint_bed_16High correlation
X is uniformly distributed Uniform
X has unique values Unique
mat_dep has 1396 (10.2%) zeros Zeros
parity has 5526 (40.2%) zeros Zeros
secd_diag has 3980 (29.0%) zeros Zeros
prim_diag has 3573 (26.0%) zeros Zeros
dep_thoughts has 12015 (87.5%) zeros Zeros

Reproduction

Analysis started2021-05-11 09:39:03.648328
Analysis finished2021-05-11 09:40:12.291363
Duration1 minute and 8.64 seconds
Software versionpandas-profiling v2.12.0
Download configurationconfig.yaml

Variables

X
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct13734
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6867.5
Minimum1
Maximum13734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.4 KiB
2021-05-11T10:40:12.372411image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile687.65
Q13434.25
median6867.5
Q310300.75
95-th percentile13047.35
Maximum13734
Range13733
Interquartile range (IQR)6866.5

Descriptive statistics

Standard deviation3964.808633
Coefficient of variation (CV)0.5773292513
Kurtosis-1.2
Mean6867.5
Median Absolute Deviation (MAD)3433.5
Skewness0
Sum94318245
Variance15719707.5
MonotocityStrictly increasing
2021-05-11T10:40:12.496330image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20491
 
< 0.1%
74331
 
< 0.1%
33391
 
< 0.1%
135801
 
< 0.1%
94861
 
< 0.1%
115351
 
< 0.1%
54001
 
< 0.1%
74491
 
< 0.1%
13061
 
< 0.1%
33551
 
< 0.1%
Other values (13724)13724
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
ValueCountFrequency (%)
137341
< 0.1%
137331
< 0.1%
137321
< 0.1%
137311
< 0.1%
137301
< 0.1%

comp_bed_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
11867 
1.0
1867 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.011867
86.4%
1.01867
 
13.6%
2021-05-11T10:40:12.701750image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:12.762492image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.011867
86.4%
1.01867
 
13.6%

Most occurring characters

ValueCountFrequency (%)
025601
62.1%
.13734
33.3%
11867
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
025601
93.2%
11867
 
6.8%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
025601
62.1%
.13734
33.3%
11867
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
025601
62.1%
.13734
33.3%
11867
 
4.5%

mat_dep
Real number (ℝ≥0)

ZEROS

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.394936929
Minimum0
Maximum28
Zeros1396
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size107.4 KiB
2021-05-11T10:40:12.829212image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.463476085 × 105
median3
Q37
95-th percentile14
Maximum28
Range28
Interquartile range (IQR)6.999975365

Descriptive statistics

Standard deviation4.66336613
Coefficient of variation (CV)1.061076918
Kurtosis1.344180214
Mean4.394936929
Median Absolute Deviation (MAD)2.999975365
Skewness1.215928363
Sum60360.06378
Variance21.74698367
MonotocityNot monotonic
2021-05-11T10:40:12.930983image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2.463476085 × 1052589
18.9%
01396
10.2%
11136
8.3%
31094
8.0%
21025
 
7.5%
4958
 
7.0%
6879
 
6.4%
5841
 
6.1%
7761
 
5.5%
8618
 
4.5%
Other values (19)2437
17.7%
ValueCountFrequency (%)
01396
10.2%
2.463476085 × 1052589
18.9%
11136
8.3%
21025
 
7.5%
31094
8.0%
ValueCountFrequency (%)
286
< 0.1%
275
< 0.1%
266
< 0.1%
246
< 0.1%
2310
0.1%

mat_age
Real number (ℝ≥0)

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.63528469
Minimum15
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.4 KiB
2021-05-11T10:40:13.035661image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile18
Q124
median28
Q331
95-th percentile36
Maximum44
Range29
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.257251261
Coefficient of variation (CV)0.1902369134
Kurtosis0.04933353386
Mean27.63528469
Median Absolute Deviation (MAD)3
Skewness-0.1918872585
Sum379543
Variance27.63869082
MonotocityNot monotonic
2021-05-11T10:40:13.137085image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
291145
 
8.3%
281130
 
8.2%
301036
 
7.5%
271006
 
7.3%
261002
 
7.3%
31950
 
6.9%
25934
 
6.8%
32750
 
5.5%
24733
 
5.3%
23642
 
4.7%
Other values (19)4406
32.1%
ValueCountFrequency (%)
15414
3.0%
1654
 
0.4%
17114
 
0.8%
18160
 
1.2%
19251
1.8%
ValueCountFrequency (%)
4413
 
0.1%
4318
 
0.1%
4228
 
0.2%
4067
0.5%
3979
0.6%

weight_16
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12859
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.96671167
Minimum31.83978081
Maximum126.0294991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.4 KiB
2021-05-11T10:40:13.261029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum31.83978081
5-th percentile32.42138596
Q132.62103081
median32.75837135
Q356.47565973
95-th percentile74.26194552
Maximum126.0294991
Range94.18971829
Interquartile range (IQR)23.85462892

Descriptive statistics

Standard deviation16.04235491
Coefficient of variation (CV)0.3648750225
Kurtosis0.9010623346
Mean43.96671167
Median Absolute Deviation (MAD)0.2535133362
Skewness1.235173515
Sum603838.818
Variance257.357151
MonotocityNot monotonic
2021-05-11T10:40:13.390871image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.7679023749
 
0.4%
32.749118825
 
0.2%
32.7659568819
 
0.1%
32.65754715
 
0.1%
32.674106614
 
0.1%
32.7529792814
 
0.1%
32.746055614
 
0.1%
32.7477836614
 
0.1%
32.7060432413
 
0.1%
32.6885757412
 
0.1%
Other values (12849)13545
98.6%
ValueCountFrequency (%)
31.839780811
< 0.1%
31.921609881
< 0.1%
31.991027831
< 0.1%
32.005538941
< 0.1%
32.010578161
< 0.1%
ValueCountFrequency (%)
126.02949912
< 0.1%
124.06305971
< 0.1%
120.73228871
< 0.1%
120.49870931
< 0.1%
120.32172091
< 0.1%

height_16
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11558
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.2753948
Minimum139.392746
Maximum201.5196218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.4 KiB
2021-05-11T10:40:13.525204image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum139.392746
5-th percentile139.5884445
Q1139.6902771
median139.8048935
Q3165.6660169
95-th percentile179.0634981
Maximum201.5196218
Range62.12687582
Interquartile range (IQR)25.97573979

Descriptive statistics

Standard deviation15.37377523
Coefficient of variation (CV)0.1016277317
Kurtosis-0.8961563774
Mean151.2753948
Median Absolute Deviation (MAD)0.1990737915
Skewness0.8087260496
Sum2077616.273
Variance236.352965
MonotocityNot monotonic
2021-05-11T10:40:13.653365image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
139.689773678
 
0.6%
139.761520470
 
0.5%
139.693405254
 
0.4%
139.761276230
 
0.2%
139.720611628
 
0.2%
139.758468626
 
0.2%
139.756668122
 
0.2%
139.765060422
 
0.2%
139.756912221
 
0.2%
139.685165420
 
0.1%
Other values (11548)13363
97.3%
ValueCountFrequency (%)
139.3927461
< 0.1%
139.41665651
< 0.1%
139.42590331
< 0.1%
139.43405151
< 0.1%
139.43489071
< 0.1%
ValueCountFrequency (%)
201.51962181
< 0.1%
200.72402051
< 0.1%
199.15405221
< 0.1%
199.05746951
< 0.1%
199.03111941
< 0.1%

iq
Real number (ℝ≥0)

Distinct3618
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.31094244
Minimum44.71630478
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.4 KiB
2021-05-11T10:40:13.790137image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum44.71630478
5-th percentile45.09387035
Q145.25292873
median78
Q3104
95-th percentile127
Maximum152
Range107.2836952
Interquartile range (IQR)58.74707127

Descriptive statistics

Standard deviation31.53117289
Coefficient of variation (CV)0.4131933361
Kurtosis-1.479305996
Mean76.31094244
Median Absolute Deviation (MAD)32.6285553
Skewness0.2892246653
Sum1048054.483
Variance994.2148638
MonotocityNot monotonic
2021-05-11T10:40:13.919481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105221
 
1.6%
101220
 
1.6%
103214
 
1.6%
102202
 
1.5%
106198
 
1.4%
107185
 
1.3%
104182
 
1.3%
94164
 
1.2%
95159
 
1.2%
108159
 
1.2%
Other values (3608)11830
86.1%
ValueCountFrequency (%)
44.716304781
< 0.1%
44.808902741
< 0.1%
44.887516021
< 0.1%
44.898632051
< 0.1%
44.900691991
< 0.1%
ValueCountFrequency (%)
1521
 
< 0.1%
1503
< 0.1%
1492
 
< 0.1%
1481
 
< 0.1%
1476
< 0.1%

comp_noint_bed_16
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
13263 
1.0
 
471

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.013263
96.6%
1.0471
 
3.4%
2021-05-11T10:40:14.127098image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:14.187548image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.013263
96.6%
1.0471
 
3.4%

Most occurring characters

ValueCountFrequency (%)
026997
65.5%
.13734
33.3%
1471
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
026997
98.3%
1471
 
1.7%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
026997
65.5%
.13734
33.3%
1471
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
026997
65.5%
.13734
33.3%
1471
 
1.1%

comp_int_bed_16
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
13263 
0.0
 
471

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
1.013263
96.6%
0.0471
 
3.4%
2021-05-11T10:40:14.345974image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:14.405973image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.013263
96.6%
0.0471
 
3.4%

Most occurring characters

ValueCountFrequency (%)
014205
34.5%
.13734
33.3%
113263
32.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
014205
51.7%
113263
48.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
014205
34.5%
.13734
33.3%
113263
32.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
014205
34.5%
.13734
33.3%
113263
32.2%

talk_phon_wend
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
10166 
1.0
3568 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.010166
74.0%
1.03568
 
26.0%
2021-05-11T10:40:14.564492image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:14.626826image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010166
74.0%
1.03568
 
26.0%

Most occurring characters

ValueCountFrequency (%)
023900
58.0%
.13734
33.3%
13568
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
023900
87.0%
13568
 
13.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
023900
58.0%
.13734
33.3%
13568
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
023900
58.0%
.13734
33.3%
13568
 
8.7%

text_wend
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
9290 
1.0
2261 
2.0
1156 
3.0
1027 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09290
67.6%
1.02261
 
16.5%
2.01156
 
8.4%
3.01027
 
7.5%
2021-05-11T10:40:14.777845image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:14.841190image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09290
67.6%
1.02261
 
16.5%
2.01156
 
8.4%
3.01027
 
7.5%

Most occurring characters

ValueCountFrequency (%)
023024
55.9%
.13734
33.3%
12261
 
5.5%
21156
 
2.8%
31027
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
023024
83.8%
12261
 
8.2%
21156
 
4.2%
31027
 
3.7%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
023024
55.9%
.13734
33.3%
12261
 
5.5%
21156
 
2.8%
31027
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
023024
55.9%
.13734
33.3%
12261
 
5.5%
21156
 
2.8%
31027
 
2.5%

talk_mob_wend
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
9651 
1.0
2728 
2.0
1355 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09651
70.3%
1.02728
 
19.9%
2.01355
 
9.9%
2021-05-11T10:40:15.011361image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:15.072640image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09651
70.3%
1.02728
 
19.9%
2.01355
 
9.9%

Most occurring characters

ValueCountFrequency (%)
023385
56.8%
.13734
33.3%
12728
 
6.6%
21355
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
023385
85.1%
12728
 
9.9%
21355
 
4.9%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
023385
56.8%
.13734
33.3%
12728
 
6.6%
21355
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
023385
56.8%
.13734
33.3%
12728
 
6.6%
21355
 
3.3%

comp_wend
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
9053 
2.0
1950 
3.0
1804 
1.0
927 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.09053
65.9%
2.01950
 
14.2%
3.01804
 
13.1%
1.0927
 
6.7%
2021-05-11T10:40:15.250853image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:15.313673image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09053
65.9%
2.01950
 
14.2%
3.01804
 
13.1%
1.0927
 
6.7%

Most occurring characters

ValueCountFrequency (%)
022787
55.3%
.13734
33.3%
21950
 
4.7%
31804
 
4.4%
1927
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
022787
83.0%
21950
 
7.1%
31804
 
6.6%
1927
 
3.4%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
022787
55.3%
.13734
33.3%
21950
 
4.7%
31804
 
4.4%
1927
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
022787
55.3%
.13734
33.3%
21950
 
4.7%
31804
 
4.4%
1927
 
2.2%

musi_wend
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
12263 
1.0
1471 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.012263
89.3%
1.01471
 
10.7%
2021-05-11T10:40:15.478675image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:15.539309image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.012263
89.3%
1.01471
 
10.7%

Most occurring characters

ValueCountFrequency (%)
025997
63.1%
.13734
33.3%
11471
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
025997
94.6%
11471
 
5.4%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
025997
63.1%
.13734
33.3%
11471
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
025997
63.1%
.13734
33.3%
11471
 
3.6%

read_wend
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
11033 
1.0
1492 
2.0
1209 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row2.0
ValueCountFrequency (%)
0.011033
80.3%
1.01492
 
10.9%
2.01209
 
8.8%
2021-05-11T10:40:15.700571image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:15.762094image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.011033
80.3%
1.01492
 
10.9%
2.01209
 
8.8%

Most occurring characters

ValueCountFrequency (%)
024767
60.1%
.13734
33.3%
11492
 
3.6%
21209
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
024767
90.2%
11492
 
5.4%
21209
 
4.4%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
024767
60.1%
.13734
33.3%
11492
 
3.6%
21209
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
024767
60.1%
.13734
33.3%
11492
 
3.6%
21209
 
2.9%

work_wend
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
10856 
2.0
1905 
3.0
 
973

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0
ValueCountFrequency (%)
1.010856
79.0%
2.01905
 
13.9%
3.0973
 
7.1%
2021-05-11T10:40:15.926681image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:15.988904image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.010856
79.0%
2.01905
 
13.9%
3.0973
 
7.1%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110856
26.3%
21905
 
4.6%
3973
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
013734
50.0%
110856
39.5%
21905
 
6.9%
3973
 
3.5%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110856
26.3%
21905
 
4.6%
3973
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110856
26.3%
21905
 
4.6%
3973
 
2.4%

alon_wend
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
10114 
2.0
1954 
3.0
1666 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row3.0
ValueCountFrequency (%)
1.010114
73.6%
2.01954
 
14.2%
3.01666
 
12.1%
2021-05-11T10:40:16.155477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:16.217820image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.010114
73.6%
2.01954
 
14.2%
3.01666
 
12.1%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110114
24.5%
21954
 
4.7%
31666
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
013734
50.0%
110114
36.8%
21954
 
7.1%
31666
 
6.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110114
24.5%
21954
 
4.7%
31666
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110114
24.5%
21954
 
4.7%
31666
 
4.0%

draw_wend
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
11080 
1.0
1814 
2.0
 
840

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.011080
80.7%
1.01814
 
13.2%
2.0840
 
6.1%
2021-05-11T10:40:16.384335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:16.446518image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.011080
80.7%
1.01814
 
13.2%
2.0840
 
6.1%

Most occurring characters

ValueCountFrequency (%)
024814
60.2%
.13734
33.3%
11814
 
4.4%
2840
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
024814
90.3%
11814
 
6.6%
2840
 
3.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
024814
60.2%
.13734
33.3%
11814
 
4.4%
2840
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
024814
60.2%
.13734
33.3%
11814
 
4.4%
2840
 
2.0%

play_wend
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
10457 
1.0
3277 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.010457
76.1%
1.03277
 
23.9%
2021-05-11T10:40:16.615594image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:16.686341image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010457
76.1%
1.03277
 
23.9%

Most occurring characters

ValueCountFrequency (%)
024191
58.7%
.13734
33.3%
13277
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
024191
88.1%
13277
 
11.9%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
024191
58.7%
.13734
33.3%
13277
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
024191
58.7%
.13734
33.3%
13277
 
8.0%

tv_wend
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
9025 
2.0
2287 
3.0
1576 
1.0
 
846

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row0.0
5th row3.0
ValueCountFrequency (%)
0.09025
65.7%
2.02287
 
16.7%
3.01576
 
11.5%
1.0846
 
6.2%
2021-05-11T10:40:16.884555image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:16.948033image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09025
65.7%
2.02287
 
16.7%
3.01576
 
11.5%
1.0846
 
6.2%

Most occurring characters

ValueCountFrequency (%)
022759
55.2%
.13734
33.3%
22287
 
5.6%
31576
 
3.8%
1846
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
022759
82.9%
22287
 
8.3%
31576
 
5.7%
1846
 
3.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
022759
55.2%
.13734
33.3%
22287
 
5.6%
31576
 
3.8%
1846
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
022759
55.2%
.13734
33.3%
22287
 
5.6%
31576
 
3.8%
1846
 
2.1%

out_win_wend
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
10398 
2.0
1978 
3.0
1358 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.010398
75.7%
2.01978
 
14.4%
3.01358
 
9.9%
2021-05-11T10:40:17.123225image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:17.184458image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.010398
75.7%
2.01978
 
14.4%
3.01358
 
9.9%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110398
25.2%
21978
 
4.8%
31358
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
013734
50.0%
110398
37.9%
21978
 
7.2%
31358
 
4.9%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110398
25.2%
21978
 
4.8%
31358
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110398
25.2%
21978
 
4.8%
31358
 
3.3%

out_sum_wend
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
9885 
1.0
3849 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09885
72.0%
1.03849
 
28.0%
2021-05-11T10:40:17.351219image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:17.412950image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09885
72.0%
1.03849
 
28.0%

Most occurring characters

ValueCountFrequency (%)
023619
57.3%
.13734
33.3%
13849
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
023619
86.0%
13849
 
14.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
023619
57.3%
.13734
33.3%
13849
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
023619
57.3%
.13734
33.3%
13849
 
9.3%

tran_wend
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
11975 
2.0
1759 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.011975
87.2%
2.01759
 
12.8%
2021-05-11T10:40:17.567571image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:17.628961image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.011975
87.2%
2.01759
 
12.8%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
111975
29.1%
21759
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
013734
50.0%
111975
43.6%
21759
 
6.4%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
111975
29.1%
21759
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
111975
29.1%
21759
 
4.3%

talk_phon_week
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
10132 
1.0
3602 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.010132
73.8%
1.03602
 
26.2%
2021-05-11T10:40:17.787473image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:17.848105image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010132
73.8%
1.03602
 
26.2%

Most occurring characters

ValueCountFrequency (%)
023866
57.9%
.13734
33.3%
13602
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
023866
86.9%
13602
 
13.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
023866
57.9%
.13734
33.3%
13602
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
023866
57.9%
.13734
33.3%
13602
 
8.7%

text_week
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
9169 
1.0
2495 
2.0
1146 
3.0
924 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09169
66.8%
1.02495
 
18.2%
2.01146
 
8.3%
3.0924
 
6.7%
2021-05-11T10:40:17.998942image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:18.062581image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09169
66.8%
1.02495
 
18.2%
2.01146
 
8.3%
3.0924
 
6.7%

Most occurring characters

ValueCountFrequency (%)
022903
55.6%
.13734
33.3%
12495
 
6.1%
21146
 
2.8%
3924
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
022903
83.4%
12495
 
9.1%
21146
 
4.2%
3924
 
3.4%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
022903
55.6%
.13734
33.3%
12495
 
6.1%
21146
 
2.8%
3924
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
022903
55.6%
.13734
33.3%
12495
 
6.1%
21146
 
2.8%
3924
 
2.2%

talk_mob_week
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
9560 
1.0
3001 
2.0
1173 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09560
69.6%
1.03001
 
21.9%
2.01173
 
8.5%
2021-05-11T10:40:18.230110image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:18.292703image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09560
69.6%
1.03001
 
21.9%
2.01173
 
8.5%

Most occurring characters

ValueCountFrequency (%)
023294
56.5%
.13734
33.3%
13001
 
7.3%
21173
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
023294
84.8%
13001
 
10.9%
21173
 
4.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
023294
56.5%
.13734
33.3%
13001
 
7.3%
21173
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
023294
56.5%
.13734
33.3%
13001
 
7.3%
21173
 
2.8%

comp_week
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
8819 
2.0
2375 
3.0
1471 
1.0
1069 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row0.0
5th row2.0
ValueCountFrequency (%)
0.08819
64.2%
2.02375
 
17.3%
3.01471
 
10.7%
1.01069
 
7.8%
2021-05-11T10:40:18.470589image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:18.533982image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08819
64.2%
2.02375
 
17.3%
3.01471
 
10.7%
1.01069
 
7.8%

Most occurring characters

ValueCountFrequency (%)
022553
54.7%
.13734
33.3%
22375
 
5.8%
31471
 
3.6%
11069
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
022553
82.1%
22375
 
8.6%
31471
 
5.4%
11069
 
3.9%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
022553
54.7%
.13734
33.3%
22375
 
5.8%
31471
 
3.6%
11069
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
022553
54.7%
.13734
33.3%
22375
 
5.8%
31471
 
3.6%
11069
 
2.6%

musi_week
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
12234 
1.0
1500 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.012234
89.1%
1.01500
 
10.9%
2021-05-11T10:40:19.102185image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:19.174230image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.012234
89.1%
1.01500
 
10.9%

Most occurring characters

ValueCountFrequency (%)
025968
63.0%
.13734
33.3%
11500
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
025968
94.5%
11500
 
5.5%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
025968
63.0%
.13734
33.3%
11500
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
025968
63.0%
.13734
33.3%
11500
 
3.6%

read_week
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
10797 
1.0
1940 
2.0
 
997

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row2.0
ValueCountFrequency (%)
0.010797
78.6%
1.01940
 
14.1%
2.0997
 
7.3%
2021-05-11T10:40:19.338336image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:19.415562image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010797
78.6%
1.01940
 
14.1%
2.0997
 
7.3%

Most occurring characters

ValueCountFrequency (%)
024531
59.5%
.13734
33.3%
11940
 
4.7%
2997
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
024531
89.3%
11940
 
7.1%
2997
 
3.6%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
024531
59.5%
.13734
33.3%
11940
 
4.7%
2997
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
024531
59.5%
.13734
33.3%
11940
 
4.7%
2997
 
2.4%

work_week
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
10435 
2.0
2330 
3.0
 
969

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0
ValueCountFrequency (%)
1.010435
76.0%
2.02330
 
17.0%
3.0969
 
7.1%
2021-05-11T10:40:19.601496image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:19.664382image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.010435
76.0%
2.02330
 
17.0%
3.0969
 
7.1%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110435
25.3%
22330
 
5.7%
3969
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
013734
50.0%
110435
38.0%
22330
 
8.5%
3969
 
3.5%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110435
25.3%
22330
 
5.7%
3969
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110435
25.3%
22330
 
5.7%
3969
 
2.4%

alon_week
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
9832 
2.0
2258 
3.0
1644 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0
ValueCountFrequency (%)
1.09832
71.6%
2.02258
 
16.4%
3.01644
 
12.0%
2021-05-11T10:40:19.840106image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:19.905746image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.09832
71.6%
2.02258
 
16.4%
3.01644
 
12.0%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
19832
23.9%
22258
 
5.5%
31644
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
013734
50.0%
19832
35.8%
22258
 
8.2%
31644
 
6.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
19832
23.9%
22258
 
5.5%
31644
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
19832
23.9%
22258
 
5.5%
31644
 
4.0%

draw_week
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
10620 
1.0
2003 
2.0
1111 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.010620
77.3%
1.02003
 
14.6%
2.01111
 
8.1%
2021-05-11T10:40:20.073504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:20.141820image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010620
77.3%
1.02003
 
14.6%
2.01111
 
8.1%

Most occurring characters

ValueCountFrequency (%)
024354
59.1%
.13734
33.3%
12003
 
4.9%
21111
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
024354
88.7%
12003
 
7.3%
21111
 
4.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
024354
59.1%
.13734
33.3%
12003
 
4.9%
21111
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
024354
59.1%
.13734
33.3%
12003
 
4.9%
21111
 
2.7%

play_week
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
9648 
1.0
4086 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.09648
70.2%
1.04086
29.8%
2021-05-11T10:40:20.309302image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:20.369604image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09648
70.2%
1.04086
29.8%

Most occurring characters

ValueCountFrequency (%)
023382
56.7%
.13734
33.3%
14086
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
023382
85.1%
14086
 
14.9%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
023382
56.7%
.13734
33.3%
14086
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
023382
56.7%
.13734
33.3%
14086
 
9.9%

tv_week
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
8856 
2.0
2634 
1.0
1174 
3.0
1070 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row0.0
5th row2.0
ValueCountFrequency (%)
0.08856
64.5%
2.02634
 
19.2%
1.01174
 
8.5%
3.01070
 
7.8%
2021-05-11T10:40:20.545635image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:20.609181image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08856
64.5%
2.02634
 
19.2%
1.01174
 
8.5%
3.01070
 
7.8%

Most occurring characters

ValueCountFrequency (%)
022590
54.8%
.13734
33.3%
22634
 
6.4%
11174
 
2.8%
31070
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
022590
82.2%
22634
 
9.6%
11174
 
4.3%
31070
 
3.9%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
022590
54.8%
.13734
33.3%
22634
 
6.4%
11174
 
2.8%
31070
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
022590
54.8%
.13734
33.3%
22634
 
6.4%
11174
 
2.8%
31070
 
2.6%

out_win_week
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
10685 
2.0
2255 
3.0
 
794

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.010685
77.8%
2.02255
 
16.4%
3.0794
 
5.8%
2021-05-11T10:40:20.781009image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:20.843868image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.010685
77.8%
2.02255
 
16.4%
3.0794
 
5.8%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110685
25.9%
22255
 
5.5%
3794
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
013734
50.0%
110685
38.9%
22255
 
8.2%
3794
 
2.9%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110685
25.9%
22255
 
5.5%
3794
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
110685
25.9%
22255
 
5.5%
3794
 
1.9%

out_sum_week
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
10810 
1.0
2924 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.010810
78.7%
1.02924
 
21.3%
2021-05-11T10:40:21.020227image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:21.081557image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010810
78.7%
1.02924
 
21.3%

Most occurring characters

ValueCountFrequency (%)
024544
59.6%
.13734
33.3%
12924
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
024544
89.4%
12924
 
10.6%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
024544
59.6%
.13734
33.3%
12924
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
024544
59.6%
.13734
33.3%
12924
 
7.1%

tran_week
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
11911 
2.0
1823 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.011911
86.7%
2.01823
 
13.3%
2021-05-11T10:40:21.243775image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:21.305154image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.011911
86.7%
2.01823
 
13.3%

Most occurring characters

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
111911
28.9%
21823
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
013734
50.0%
111911
43.4%
21823
 
6.6%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
111911
28.9%
21823
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
.13734
33.3%
013734
33.3%
111911
28.9%
21823
 
4.4%

pat_pres_10
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
7341 
1.0
6393 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.07341
53.5%
1.06393
46.5%
2021-05-11T10:40:21.457796image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:21.518534image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07341
53.5%
1.06393
46.5%

Most occurring characters

ValueCountFrequency (%)
021075
51.2%
.13734
33.3%
16393
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
021075
76.7%
16393
 
23.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
021075
51.2%
.13734
33.3%
16393
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
021075
51.2%
.13734
33.3%
16393
 
15.5%

pat_pres_8
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
7344 
1.0
6390 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
0.07344
53.5%
1.06390
46.5%
2021-05-11T10:40:21.671000image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:21.732658image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07344
53.5%
1.06390
46.5%

Most occurring characters

ValueCountFrequency (%)
021078
51.2%
.13734
33.3%
16390
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
021078
76.7%
16390
 
23.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
021078
51.2%
.13734
33.3%
16390
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
021078
51.2%
.13734
33.3%
16390
 
15.5%

pat_pres
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
8270 
0.0
5464 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
1.08270
60.2%
0.05464
39.8%
2021-05-11T10:40:21.899009image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:21.960397image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.08270
60.2%
0.05464
39.8%

Most occurring characters

ValueCountFrequency (%)
019198
46.6%
.13734
33.3%
18270
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
019198
69.9%
18270
30.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
019198
46.6%
.13734
33.3%
18270
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
019198
46.6%
.13734
33.3%
18270
20.1%

num_home
Real number (ℝ≥0)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.19957769
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.4 KiB
2021-05-11T10:40:22.017659image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q34
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.666114664
Coefficient of variation (CV)0.520729554
Kurtosis-0.8786097491
Mean3.19957769
Median Absolute Deviation (MAD)1
Skewness-0.0568298581
Sum43943
Variance2.775938073
MonotocityNot monotonic
2021-05-11T10:40:22.105544image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
45080
37.0%
14171
30.4%
51909
 
13.9%
31443
 
10.5%
6574
 
4.2%
2368
 
2.7%
7132
 
1.0%
839
 
0.3%
918
 
0.1%
ValueCountFrequency (%)
14171
30.4%
2368
 
2.7%
31443
 
10.5%
45080
37.0%
51909
 
13.9%
ValueCountFrequency (%)
918
 
0.1%
839
 
0.3%
7132
 
1.0%
6574
 
4.2%
51909
13.9%

mat_anx_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
11379 
1.0
2355 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.011379
82.9%
1.02355
 
17.1%
2021-05-11T10:40:22.289740image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:22.350359image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.011379
82.9%
1.02355
 
17.1%

Most occurring characters

ValueCountFrequency (%)
025113
61.0%
.13734
33.3%
12355
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
025113
91.4%
12355
 
8.6%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
025113
61.0%
.13734
33.3%
12355
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
025113
61.0%
.13734
33.3%
12355
 
5.7%

mat_anx_18m
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
11676 
1.0
2058 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.011676
85.0%
1.02058
 
15.0%
2021-05-11T10:40:22.505146image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:22.566097image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.011676
85.0%
1.02058
 
15.0%

Most occurring characters

ValueCountFrequency (%)
025410
61.7%
.13734
33.3%
12058
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
025410
92.5%
12058
 
7.5%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
025410
61.7%
.13734
33.3%
12058
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
025410
61.7%
.13734
33.3%
12058
 
5.0%

mat_anx_8m
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
11795 
1.0
1939 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.011795
85.9%
1.01939
 
14.1%
2021-05-11T10:40:22.725808image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:22.786593image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.011795
85.9%
1.01939
 
14.1%

Most occurring characters

ValueCountFrequency (%)
025529
62.0%
.13734
33.3%
11939
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
025529
92.9%
11939
 
7.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
025529
62.0%
.13734
33.3%
11939
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
025529
62.0%
.13734
33.3%
11939
 
4.7%

agg_score
Real number (ℝ≥0)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.419930707
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.4 KiB
2021-05-11T10:40:22.845232image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.000108719
Q16
median9
Q311
95-th percentile13
Maximum15
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.375025732
Coefficient of variation (CV)0.4008377087
Kurtosis-0.8997305303
Mean8.419930707
Median Absolute Deviation (MAD)2
Skewness-0.5215761113
Sum115639.3283
Variance11.39079869
MonotocityNot monotonic
2021-05-11T10:40:22.941441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
93144
22.9%
3.0001087193020
22.0%
101756
12.8%
121755
12.8%
111578
11.5%
8722
 
5.3%
13459
 
3.3%
7418
 
3.0%
6272
 
2.0%
14199
 
1.4%
Other values (4)411
 
3.0%
ValueCountFrequency (%)
366
 
0.5%
3.0001087193020
22.0%
475
 
0.5%
5133
 
1.0%
6272
 
2.0%
ValueCountFrequency (%)
15137
 
1.0%
14199
 
1.4%
13459
 
3.3%
121755
12.8%
111578
11.5%

emot_cruel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
12725 
1.0
 
1009

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.012725
92.7%
1.01009
 
7.3%
2021-05-11T10:40:23.127192image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:23.187352image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.012725
92.7%
1.01009
 
7.3%

Most occurring characters

ValueCountFrequency (%)
026459
64.2%
.13734
33.3%
11009
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
026459
96.3%
11009
 
3.7%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
026459
64.2%
.13734
33.3%
11009
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
026459
64.2%
.13734
33.3%
11009
 
2.4%

phys_cruel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
13442 
1.0
 
292

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.013442
97.9%
1.0292
 
2.1%
2021-05-11T10:40:23.351926image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:23.412584image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.013442
97.9%
1.0292
 
2.1%

Most occurring characters

ValueCountFrequency (%)
027176
66.0%
.13734
33.3%
1292
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
027176
98.9%
1292
 
1.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
027176
66.0%
.13734
33.3%
1292
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
027176
66.0%
.13734
33.3%
1292
 
0.7%

mat_anx_0m
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
11425 
1.0
2309 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.011425
83.2%
1.02309
 
16.8%
2021-05-11T10:40:23.564446image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:23.627175image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.011425
83.2%
1.02309
 
16.8%

Most occurring characters

ValueCountFrequency (%)
025159
61.1%
.13734
33.3%
12309
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
025159
91.6%
12309
 
8.4%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
025159
61.1%
.13734
33.3%
12309
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
025159
61.1%
.13734
33.3%
12309
 
5.6%

pat_ses
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.467744284
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.4 KiB
2021-05-11T10:40:23.680837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.327887908
Coefficient of variation (CV)0.5380978559
Kurtosis-0.01168017117
Mean2.467744284
Median Absolute Deviation (MAD)1
Skewness0.784778586
Sum33892
Variance1.763286297
MonotocityNot monotonic
2021-05-11T10:40:23.762779image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
13912
28.5%
23837
27.9%
33385
24.6%
41206
 
8.8%
51064
 
7.7%
6303
 
2.2%
727
 
0.2%
ValueCountFrequency (%)
13912
28.5%
23837
27.9%
33385
24.6%
41206
 
8.8%
51064
 
7.7%
ValueCountFrequency (%)
727
 
0.2%
6303
 
2.2%
51064
 
7.7%
41206
 
8.8%
33385
24.6%

mat_ses
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.684651231
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.4 KiB
2021-05-11T10:40:23.847453image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.442841008
Coefficient of variation (CV)0.537440764
Kurtosis-1.28753909
Mean2.684651231
Median Absolute Deviation (MAD)1
Skewness0.2238505818
Sum36871
Variance2.081790175
MonotocityNot monotonic
2021-05-11T10:40:23.924377image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
44513
32.9%
14156
30.3%
23068
22.3%
5935
 
6.8%
3841
 
6.1%
6218
 
1.6%
73
 
< 0.1%
ValueCountFrequency (%)
14156
30.3%
23068
22.3%
3841
 
6.1%
44513
32.9%
5935
 
6.8%
ValueCountFrequency (%)
73
 
< 0.1%
6218
 
1.6%
5935
 
6.8%
44513
32.9%
3841
 
6.1%

pat_edu
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
4910 
2.0
3225 
1.0
2588 
4.0
2026 
3.0
985 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row1.0
4th row1.0
5th row2.0
ValueCountFrequency (%)
0.04910
35.8%
2.03225
23.5%
1.02588
18.8%
4.02026
14.8%
3.0985
 
7.2%
2021-05-11T10:40:24.130834image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:24.195523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.04910
35.8%
2.03225
23.5%
1.02588
18.8%
4.02026
14.8%
3.0985
 
7.2%

Most occurring characters

ValueCountFrequency (%)
018644
45.3%
.13734
33.3%
23225
 
7.8%
12588
 
6.3%
42026
 
4.9%
3985
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
018644
67.9%
23225
 
11.7%
12588
 
9.4%
42026
 
7.4%
3985
 
3.6%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
018644
45.3%
.13734
33.3%
23225
 
7.8%
12588
 
6.3%
42026
 
4.9%
3985
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
018644
45.3%
.13734
33.3%
23225
 
7.8%
12588
 
6.3%
42026
 
4.9%
3985
 
2.4%

mat_edu
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
4362 
0.0
3846 
2.0
2832 
4.0
1422 
3.0
1272 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row1.0
4th row2.0
5th row2.0
ValueCountFrequency (%)
1.04362
31.8%
0.03846
28.0%
2.02832
20.6%
4.01422
 
10.4%
3.01272
 
9.3%
2021-05-11T10:40:24.383048image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:24.448226image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.04362
31.8%
0.03846
28.0%
2.02832
20.6%
4.01422
 
10.4%
3.01272
 
9.3%

Most occurring characters

ValueCountFrequency (%)
017580
42.7%
.13734
33.3%
14362
 
10.6%
22832
 
6.9%
41422
 
3.5%
31272
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
017580
64.0%
14362
 
15.9%
22832
 
10.3%
41422
 
5.2%
31272
 
4.6%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
017580
42.7%
.13734
33.3%
14362
 
10.6%
22832
 
6.9%
41422
 
3.5%
31272
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
017580
42.7%
.13734
33.3%
14362
 
10.6%
22832
 
6.9%
41422
 
3.5%
31272
 
3.1%

parity
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7699876063
Minimum0
Maximum22
Zeros5526
Zeros (%)40.2%
Negative0
Negative (%)0.0%
Memory size107.4 KiB
2021-05-11T10:40:24.524889image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum22
Range22
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9936950268
Coefficient of variation (CV)1.290533794
Kurtosis34.80820645
Mean0.7699876063
Median Absolute Deviation (MAD)1
Skewness2.962337727
Sum10575.00979
Variance0.9874298062
MonotocityNot monotonic
2021-05-11T10:40:24.608525image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
05526
40.2%
14543
33.1%
21777
 
12.9%
8.222839824 × 1061190
 
8.7%
3471
 
3.4%
4140
 
1.0%
566
 
0.5%
612
 
0.1%
84
 
< 0.1%
72
 
< 0.1%
Other values (2)3
 
< 0.1%
ValueCountFrequency (%)
05526
40.2%
8.222839824 × 1061190
 
8.7%
14543
33.1%
21777
 
12.9%
3471
 
3.4%
ValueCountFrequency (%)
222
 
< 0.1%
131
 
< 0.1%
84
 
< 0.1%
72
 
< 0.1%
612
0.1%

dep_band_15
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
13242 
1.0
 
403
2.0
 
89

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.013242
96.4%
1.0403
 
2.9%
2.089
 
0.6%
2021-05-11T10:40:24.803640image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:24.867023image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.013242
96.4%
1.0403
 
2.9%
2.089
 
0.6%

Most occurring characters

ValueCountFrequency (%)
026976
65.5%
.13734
33.3%
1403
 
1.0%
289
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
026976
98.2%
1403
 
1.5%
289
 
0.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
026976
65.5%
.13734
33.3%
1403
 
1.0%
289
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
026976
65.5%
.13734
33.3%
1403
 
1.0%
289
 
0.2%

dep_band_13
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
13306 
1.0
 
355
2.0
 
73

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.013306
96.9%
1.0355
 
2.6%
2.073
 
0.5%
2021-05-11T10:40:25.042005image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:25.104291image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.013306
96.9%
1.0355
 
2.6%
2.073
 
0.5%

Most occurring characters

ValueCountFrequency (%)
027040
65.6%
.13734
33.3%
1355
 
0.9%
273
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
027040
98.4%
1355
 
1.3%
273
 
0.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
027040
65.6%
.13734
33.3%
1355
 
0.9%
273
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
027040
65.6%
.13734
33.3%
1355
 
0.9%
273
 
0.2%

dep_band_10
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
13368 
1.0
 
294
2.0
 
72

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.013368
97.3%
1.0294
 
2.1%
2.072
 
0.5%
2021-05-11T10:40:25.283688image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:25.346724image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.013368
97.3%
1.0294
 
2.1%
2.072
 
0.5%

Most occurring characters

ValueCountFrequency (%)
027102
65.8%
.13734
33.3%
1294
 
0.7%
272
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
027102
98.7%
1294
 
1.1%
272
 
0.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
027102
65.8%
.13734
33.3%
1294
 
0.7%
272
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
027102
65.8%
.13734
33.3%
1294
 
0.7%
272
 
0.2%

dep_band_07
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
13497 
1.0
 
188
2.0
 
49

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.013497
98.3%
1.0188
 
1.4%
2.049
 
0.4%
2021-05-11T10:40:25.524782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:25.586782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.013497
98.3%
1.0188
 
1.4%
2.049
 
0.4%

Most occurring characters

ValueCountFrequency (%)
027231
66.1%
.13734
33.3%
1188
 
0.5%
249
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
027231
99.1%
1188
 
0.7%
249
 
0.2%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
027231
66.1%
.13734
33.3%
1188
 
0.5%
249
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
027231
66.1%
.13734
33.3%
1188
 
0.5%
249
 
0.1%

anx_band_15
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
10387 
1.0
3248 
2.0
 
99

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.010387
75.6%
1.03248
 
23.6%
2.099
 
0.7%
2021-05-11T10:40:25.757482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:25.819200image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010387
75.6%
1.03248
 
23.6%
2.099
 
0.7%

Most occurring characters

ValueCountFrequency (%)
024121
58.5%
.13734
33.3%
13248
 
7.9%
299
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
024121
87.8%
13248
 
11.8%
299
 
0.4%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
024121
58.5%
.13734
33.3%
13248
 
7.9%
299
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
024121
58.5%
.13734
33.3%
13248
 
7.9%
299
 
0.2%

anx_band_13
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
9472 
1.0
4159 
2.0
 
103

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09472
69.0%
1.04159
30.3%
2.0103
 
0.7%
2021-05-11T10:40:25.975724image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:26.037474image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09472
69.0%
1.04159
30.3%
2.0103
 
0.7%

Most occurring characters

ValueCountFrequency (%)
023206
56.3%
.13734
33.3%
14159
 
10.1%
2103
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
023206
84.5%
14159
 
15.1%
2103
 
0.4%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
023206
56.3%
.13734
33.3%
14159
 
10.1%
2103
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
023206
56.3%
.13734
33.3%
14159
 
10.1%
2103
 
0.2%

anx_band_10
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
8905 
1.0
4695 
2.0
 
134

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.08905
64.8%
1.04695
34.2%
2.0134
 
1.0%
2021-05-11T10:40:26.193916image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:26.255219image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08905
64.8%
1.04695
34.2%
2.0134
 
1.0%

Most occurring characters

ValueCountFrequency (%)
022639
54.9%
.13734
33.3%
14695
 
11.4%
2134
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
022639
82.4%
14695
 
17.1%
2134
 
0.5%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
022639
54.9%
.13734
33.3%
14695
 
11.4%
2134
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
022639
54.9%
.13734
33.3%
14695
 
11.4%
2134
 
0.3%

anx_band_07
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
9310 
1.0
4277 
2.0
 
147

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.09310
67.8%
1.04277
31.1%
2.0147
 
1.1%
2021-05-11T10:40:26.412251image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:26.473910image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09310
67.8%
1.04277
31.1%
2.0147
 
1.1%

Most occurring characters

ValueCountFrequency (%)
023044
55.9%
.13734
33.3%
14277
 
10.4%
2147
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
023044
83.9%
14277
 
15.6%
2147
 
0.5%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
023044
55.9%
.13734
33.3%
14277
 
10.4%
2147
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
023044
55.9%
.13734
33.3%
14277
 
10.4%
2147
 
0.4%

exercise
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
9011 
3.0
2528 
4.0
1195 
2.0
 
708
1.0
 
292

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row0.0
3rd row0.0
4th row0.0
5th row3.0
ValueCountFrequency (%)
0.09011
65.6%
3.02528
 
18.4%
4.01195
 
8.7%
2.0708
 
5.2%
1.0292
 
2.1%
2021-05-11T10:40:26.634628image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:26.699899image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09011
65.6%
3.02528
 
18.4%
4.01195
 
8.7%
2.0708
 
5.2%
1.0292
 
2.1%

Most occurring characters

ValueCountFrequency (%)
022745
55.2%
.13734
33.3%
32528
 
6.1%
41195
 
2.9%
2708
 
1.7%
1292
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
022745
82.8%
32528
 
9.2%
41195
 
4.4%
2708
 
2.6%
1292
 
1.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
022745
55.2%
.13734
33.3%
32528
 
6.1%
41195
 
2.9%
2708
 
1.7%
1292
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
022745
55.2%
.13734
33.3%
32528
 
6.1%
41195
 
2.9%
2708
 
1.7%
1292
 
0.7%

child_bull
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
12821 
1.0
 
913

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.012821
93.4%
1.0913
 
6.6%
2021-05-11T10:40:26.870367image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:26.932590image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.012821
93.4%
1.0913
 
6.6%

Most occurring characters

ValueCountFrequency (%)
026555
64.5%
.13734
33.3%
1913
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
026555
96.7%
1913
 
3.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
026555
64.5%
.13734
33.3%
1913
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
026555
64.5%
.13734
33.3%
1913
 
2.2%

phone_14_wend
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
8676 
1.0
3828 
2.0
1230 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.08676
63.2%
1.03828
27.9%
2.01230
 
9.0%
2021-05-11T10:40:27.101991image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:27.687365image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08676
63.2%
1.03828
27.9%
2.01230
 
9.0%

Most occurring characters

ValueCountFrequency (%)
022410
54.4%
.13734
33.3%
13828
 
9.3%
21230
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
022410
81.6%
13828
 
13.9%
21230
 
4.5%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
022410
54.4%
.13734
33.3%
13828
 
9.3%
21230
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
022410
54.4%
.13734
33.3%
13828
 
9.3%
21230
 
3.0%

phone_14_week
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
8688 
1.0
3949 
2.0
1097 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.08688
63.3%
1.03949
28.8%
2.01097
 
8.0%
2021-05-11T10:40:27.884598image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:27.947519image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08688
63.3%
1.03949
28.8%
2.01097
 
8.0%

Most occurring characters

ValueCountFrequency (%)
022422
54.4%
.13734
33.3%
13949
 
9.6%
21097
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
022422
81.6%
13949
 
14.4%
21097
 
4.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
022422
54.4%
.13734
33.3%
13949
 
9.6%
21097
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
022422
54.4%
.13734
33.3%
13949
 
9.6%
21097
 
2.7%

musi_13
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
10170 
1.0
3564 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.010170
74.0%
1.03564
 
26.0%
2021-05-11T10:40:28.113967image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:28.176082image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010170
74.0%
1.03564
 
26.0%

Most occurring characters

ValueCountFrequency (%)
023904
58.0%
.13734
33.3%
13564
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
023904
87.0%
13564
 
13.0%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
023904
58.0%
.13734
33.3%
13564
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
023904
58.0%
.13734
33.3%
13564
 
8.7%

tv_bed_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
9434 
1.0
4300 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.09434
68.7%
1.04300
31.3%
2021-05-11T10:40:28.327337image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:28.388022image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09434
68.7%
1.04300
31.3%

Most occurring characters

ValueCountFrequency (%)
023168
56.2%
.13734
33.3%
14300
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
023168
84.3%
14300
 
15.7%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
023168
56.2%
.13734
33.3%
14300
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
023168
56.2%
.13734
33.3%
14300
 
10.4%

own_mob
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
11623 
1.0
2111 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.011623
84.6%
1.02111
 
15.4%
2021-05-11T10:40:28.544223image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:28.604407image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.011623
84.6%
1.02111
 
15.4%

Most occurring characters

ValueCountFrequency (%)
025357
61.5%
.13734
33.3%
12111
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
025357
92.3%
12111
 
7.7%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
025357
61.5%
.13734
33.3%
12111
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
025357
61.5%
.13734
33.3%
12111
 
5.1%

has_dep_diag
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
13345 
1.0
 
389

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.013345
97.2%
1.0389
 
2.8%
2021-05-11T10:40:28.763316image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:28.824163image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.013345
97.2%
1.0389
 
2.8%

Most occurring characters

ValueCountFrequency (%)
027079
65.7%
.13734
33.3%
1389
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
027079
98.6%
1389
 
1.4%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
027079
65.7%
.13734
33.3%
1389
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
027079
65.7%
.13734
33.3%
1389
 
0.9%

secd_diag
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1372535223
Minimum0
Maximum9
Zeros3980
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size107.4 KiB
2021-05-11T10:40:28.883184image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.324361271 × 106
Q34.324361271 × 106
95-th percentile4.324361271 × 106
Maximum9
Range9
Interquartile range (IQR)4.324361271 × 106

Descriptive statistics

Standard deviation0.8900966347
Coefficient of variation (CV)6.485054955
Kurtosis62.36043722
Mean0.1372535223
Median Absolute Deviation (MAD)0
Skewness7.729090288
Sum1885.039875
Variance0.7922720191
MonotocityNot monotonic
2021-05-11T10:40:28.964891image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4.324361271 × 1069221
67.1%
03980
29.0%
1246
 
1.8%
894
 
0.7%
462
 
0.5%
544
 
0.3%
243
 
0.3%
922
 
0.2%
619
 
0.1%
73
 
< 0.1%
ValueCountFrequency (%)
03980
29.0%
4.324361271 × 1069221
67.1%
1246
 
1.8%
243
 
0.3%
462
 
0.5%
ValueCountFrequency (%)
922
 
0.2%
894
0.7%
73
 
< 0.1%
619
 
0.1%
544
0.3%

prim_diag
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4277021674
Minimum0
Maximum12
Zeros3573
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size107.4 KiB
2021-05-11T10:40:29.047166image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.676820703 × 106
Q36.676820703 × 106
95-th percentile2
Maximum12
Range12
Interquartile range (IQR)6.676820703 × 106

Descriptive statistics

Standard deviation1.933611066
Coefficient of variation (CV)4.520928845
Kurtosis22.74330332
Mean0.4277021674
Median Absolute Deviation (MAD)0
Skewness4.856945102
Sum5874.061567
Variance3.738851754
MonotocityNot monotonic
2021-05-11T10:40:29.143723image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
6.676820703 × 1069221
67.1%
03573
 
26.0%
1240
 
1.7%
11181
 
1.3%
10134
 
1.0%
5101
 
0.7%
299
 
0.7%
1274
 
0.5%
666
 
0.5%
822
 
0.2%
Other values (3)23
 
0.2%
ValueCountFrequency (%)
03573
 
26.0%
6.676820703 × 1069221
67.1%
1240
 
1.7%
299
 
0.7%
411
 
0.1%
ValueCountFrequency (%)
1274
0.5%
11181
1.3%
10134
1.0%
96
 
< 0.1%
822
 
0.2%

panic_score
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
13634 
1.0
 
39
2.0
 
37
3.0
 
14
4.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.013634
99.3%
1.039
 
0.3%
2.037
 
0.3%
3.014
 
0.1%
4.010
 
0.1%
2021-05-11T10:40:29.356906image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:29.423144image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.013634
99.3%
1.039
 
0.3%
2.037
 
0.3%
3.014
 
0.1%
4.010
 
0.1%

Most occurring characters

ValueCountFrequency (%)
027368
66.4%
.13734
33.3%
139
 
0.1%
237
 
0.1%
314
 
< 0.1%
410
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
027368
99.6%
139
 
0.1%
237
 
0.1%
314
 
0.1%
410
 
< 0.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
027368
66.4%
.13734
33.3%
139
 
0.1%
237
 
0.1%
314
 
< 0.1%
410
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
027368
66.4%
.13734
33.3%
139
 
0.1%
237
 
0.1%
314
 
< 0.1%
410
 
< 0.1%

dep_thoughts
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.253968254
Minimum0
Maximum5
Zeros12015
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size107.4 KiB
2021-05-11T10:40:29.490682image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.810985465
Coefficient of variation (CV)3.193255268
Kurtosis14.90710621
Mean0.253968254
Median Absolute Deviation (MAD)0
Skewness3.80855082
Sum3488
Variance0.6576974245
MonotocityNot monotonic
2021-05-11T10:40:29.577047image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
012015
87.5%
1897
 
6.5%
2282
 
2.1%
3225
 
1.6%
4223
 
1.6%
592
 
0.7%
ValueCountFrequency (%)
012015
87.5%
1897
 
6.5%
2282
 
2.1%
3225
 
1.6%
4223
 
1.6%
ValueCountFrequency (%)
592
 
0.7%
4223
 
1.6%
3225
 
1.6%
2282
 
2.1%
1897
6.5%

dep_score
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
12684 
1.0
 
488
2.0
 
299
3.0
 
163
4.0
 
100

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.012684
92.4%
1.0488
 
3.6%
2.0299
 
2.2%
3.0163
 
1.2%
4.0100
 
0.7%
2021-05-11T10:40:29.773267image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:29.837517image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.012684
92.4%
1.0488
 
3.6%
2.0299
 
2.2%
3.0163
 
1.2%
4.0100
 
0.7%

Most occurring characters

ValueCountFrequency (%)
026418
64.1%
.13734
33.3%
1488
 
1.2%
2299
 
0.7%
3163
 
0.4%
4100
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
026418
96.2%
1488
 
1.8%
2299
 
1.1%
3163
 
0.6%
4100
 
0.4%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
026418
64.1%
.13734
33.3%
1488
 
1.2%
2299
 
0.7%
3163
 
0.4%
4100
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
026418
64.1%
.13734
33.3%
1488
 
1.2%
2299
 
0.7%
3163
 
0.4%
4100
 
0.2%

comp_house
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
13167 
1.0
 
567

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.013167
95.9%
1.0567
 
4.1%
2021-05-11T10:40:30.011733image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:30.072377image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.013167
95.9%
1.0567
 
4.1%

Most occurring characters

ValueCountFrequency (%)
026901
65.3%
.13734
33.3%
1567
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
026901
97.9%
1567
 
2.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
026901
65.3%
.13734
33.3%
1567
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
026901
65.3%
.13734
33.3%
1567
 
1.4%

tv_bed_16
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
10086 
1.0
3648 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.010086
73.4%
1.03648
 
26.6%
2021-05-11T10:40:30.233495image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:30.295960image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010086
73.4%
1.03648
 
26.6%

Most occurring characters

ValueCountFrequency (%)
023820
57.8%
.13734
33.3%
13648
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
023820
86.7%
13648
 
13.3%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
023820
57.8%
.13734
33.3%
13648
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
023820
57.8%
.13734
33.3%
13648
 
8.9%

creat_14
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
10281 
1.0
3453 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.010281
74.9%
1.03453
 
25.1%
2021-05-11T10:40:30.454518image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:30.515462image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010281
74.9%
1.03453
 
25.1%

Most occurring characters

ValueCountFrequency (%)
024015
58.3%
.13734
33.3%
13453
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
024015
87.4%
13453
 
12.6%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
024015
58.3%
.13734
33.3%
13453
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
024015
58.3%
.13734
33.3%
13453
 
8.4%

comp_games
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
8686 
1.0
5048 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.08686
63.2%
1.05048
36.8%
2021-05-11T10:40:30.680070image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:30.741452image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08686
63.2%
1.05048
36.8%

Most occurring characters

ValueCountFrequency (%)
022420
54.4%
.13734
33.3%
15048
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
022420
81.6%
15048
 
18.4%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
022420
54.4%
.13734
33.3%
15048
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
022420
54.4%
.13734
33.3%
15048
 
12.3%

fam_tv_eve
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
1.0
8199 
0.0
3127 
2.0
2408 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
1.08199
59.7%
0.03127
 
22.8%
2.02408
 
17.5%
2021-05-11T10:40:30.908447image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:30.969890image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.08199
59.7%
0.03127
 
22.8%
2.02408
 
17.5%

Most occurring characters

ValueCountFrequency (%)
016861
40.9%
.13734
33.3%
18199
19.9%
22408
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
016861
61.4%
18199
29.8%
22408
 
8.8%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
016861
40.9%
.13734
33.3%
18199
19.9%
22408
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
016861
40.9%
.13734
33.3%
18199
19.9%
22408
 
5.8%

fam_tv_aft
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
6496 
2.0
5065 
1.0
2173 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row2.0
4th row0.0
5th row2.0
ValueCountFrequency (%)
0.06496
47.3%
2.05065
36.9%
1.02173
 
15.8%
2021-05-11T10:40:31.147959image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:31.209918image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.06496
47.3%
2.05065
36.9%
1.02173
 
15.8%

Most occurring characters

ValueCountFrequency (%)
020230
49.1%
.13734
33.3%
25065
 
12.3%
12173
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
020230
73.6%
25065
 
18.4%
12173
 
7.9%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
020230
49.1%
.13734
33.3%
25065
 
12.3%
12173
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
020230
49.1%
.13734
33.3%
25065
 
12.3%
12173
 
5.3%

fam_tv_mor
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
5831 
2.0
4360 
1.0
3543 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row0.0
5th row2.0
ValueCountFrequency (%)
0.05831
42.5%
2.04360
31.7%
1.03543
25.8%
2021-05-11T10:40:31.368052image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:31.431336image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.05831
42.5%
2.04360
31.7%
1.03543
25.8%

Most occurring characters

ValueCountFrequency (%)
019565
47.5%
.13734
33.3%
24360
 
10.6%
13543
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
019565
71.2%
24360
 
15.9%
13543
 
12.9%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
019565
47.5%
.13734
33.3%
24360
 
10.6%
13543
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
019565
47.5%
.13734
33.3%
24360
 
10.6%
13543
 
8.6%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
7102 
1.0
6632 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
0.07102
51.7%
1.06632
48.3%
2021-05-11T10:40:31.588502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:31.650036image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07102
51.7%
1.06632
48.3%

Most occurring characters

ValueCountFrequency (%)
020836
50.6%
.13734
33.3%
16632
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
020836
75.9%
16632
 
24.1%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
020836
50.6%
.13734
33.3%
16632
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
020836
50.6%
.13734
33.3%
16632
 
16.1%

birth_order
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
0.0
13532 
1.0
 
202

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41202
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.013532
98.5%
1.0202
 
1.5%
2021-05-11T10:40:31.813824image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T10:40:31.874653image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.013532
98.5%
1.0202
 
1.5%

Most occurring characters

ValueCountFrequency (%)
027266
66.2%
.13734
33.3%
1202
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27468
66.7%
Other Punctuation13734
33.3%

Most frequent character per category

ValueCountFrequency (%)
027266
99.3%
1202
 
0.7%
ValueCountFrequency (%)
.13734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41202
100.0%

Most frequent character per script

ValueCountFrequency (%)
027266
66.2%
.13734
33.3%
1202
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41202
100.0%

Most frequent character per block

ValueCountFrequency (%)
027266
66.2%
.13734
33.3%
1202
 
0.5%

Interactions

2021-05-11T10:39:47.460123image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-11T10:39:47.913857image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-11T10:39:49.499840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-11T10:39:49.601870image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-11T10:39:49.805898image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-11T10:39:49.903141image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-11T10:39:50.102953image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-11T10:39:50.204468image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-11T10:39:50.484725image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-11T10:39:50.713870image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-11T10:39:50.820757image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-11T10:39:51.037798image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-11T10:40:06.957919image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-05-11T10:40:32.096323image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-11T10:40:33.429533image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-11T10:40:34.753324image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-11T10:40:36.080573image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-11T10:40:37.371060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-11T10:40:07.472002image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-11T10:40:10.918008image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Xcomp_bed_9mat_depmat_ageweight_16height_16iqcomp_noint_bed_16comp_int_bed_16talk_phon_wendtext_wendtalk_mob_wendcomp_wendmusi_wendread_wendwork_wendalon_wenddraw_wendplay_wendtv_wendout_win_wendout_sum_wendtran_wendtalk_phon_weektext_weektalk_mob_weekcomp_weekmusi_weekread_weekwork_weekalon_weekdraw_weekplay_weektv_weekout_win_weekout_sum_weektran_weekpat_pres_10pat_pres_8pat_presnum_homemat_anx_1mat_anx_18mmat_anx_8magg_scoreemot_cruelphys_cruelmat_anx_0mpat_sesmat_sespat_edumat_eduparitydep_band_15dep_band_13dep_band_10dep_band_07anx_band_15anx_band_13anx_band_10anx_band_07exercisechild_bullphone_14_wendphone_14_weekmusi_13tv_bed_9own_mobhas_dep_diagsecd_diagprim_diagpanic_scoredep_thoughtsdep_scorecomp_housetv_bed_16creat_14comp_gamesfam_tv_evefam_tv_aftfam_tv_morsexbirth_order
010.03.00000030.059.294132181.602831107.0000000.01.01.01.01.02.00.01.03.02.00.01.02.03.01.01.00.01.01.02.00.01.03.02.00.01.02.03.01.01.01.01.01.04.00.00.00.011.0000000.00.00.03.02.02.02.00.00.00.00.00.00.00.01.01.04.00.00.00.00.00.00.00.00.0000000.0000000.00.00.01.01.01.00.01.00.02.00.00.0
120.09.00000026.032.752800139.65950085.0000000.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.00.01.00.00.00.09.0000000.00.00.01.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0000040.0000070.00.00.00.00.00.00.00.00.00.00.01.0
230.03.00000024.032.667496139.71913145.2727930.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.00.01.00.00.00.08.0000000.00.00.03.05.01.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0000040.0000070.00.00.00.00.00.00.01.02.00.01.00.0
340.00.00002522.049.812426160.22418645.1793860.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.01.00.01.00.00.00.03.0001090.00.00.03.01.01.02.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0000040.0000070.00.00.00.00.00.00.00.00.00.01.00.0
450.08.00000031.062.270030191.703227132.0000001.00.00.00.00.01.00.02.02.03.00.00.03.01.00.01.00.00.00.02.00.02.02.02.00.01.02.01.00.01.00.00.00.04.00.00.00.09.0000001.00.00.03.05.02.02.01.00.00.00.00.01.00.00.00.03.00.00.00.00.01.01.00.00.0000040.0000070.00.00.00.00.01.00.01.02.02.00.00.0
560.010.00000030.078.936613169.722373106.0000000.01.01.01.01.01.00.01.01.03.00.01.03.02.01.01.01.01.01.02.00.01.03.01.00.01.02.03.01.02.01.01.00.01.00.00.00.011.0000000.00.00.04.05.03.03.03.00.01.00.00.00.01.01.00.02.00.00.00.00.00.00.00.00.0000000.0000000.00.00.00.00.00.01.01.00.01.00.00.0
670.00.00000036.032.871513139.88897746.1270220.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.01.03.00.00.00.013.0000000.00.00.02.04.01.03.02.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0000040.0000070.00.00.00.00.00.01.02.02.00.01.00.0
780.00.00002526.084.299663186.32728498.0000000.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.00.04.01.00.00.03.0001090.00.00.03.02.02.00.00.00.00.00.00.01.00.00.00.00.00.01.02.01.01.00.00.00.0000000.0000000.00.00.00.01.00.01.02.02.02.00.00.0
890.04.00000030.070.657258176.027653108.0000000.01.01.01.01.03.01.00.03.02.01.01.02.03.01.02.01.03.02.03.01.00.03.02.01.01.02.03.01.02.01.01.01.04.00.00.00.010.0000000.00.00.02.04.02.03.00.01.00.00.00.01.00.00.00.04.00.00.00.00.00.00.00.00.0000005.0000000.00.00.00.01.01.00.00.02.02.01.00.0
9100.00.00000030.070.529403187.778207113.0000000.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.01.03.00.00.00.013.0000000.00.00.01.02.04.04.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.00.00.00.0000040.0000070.00.00.00.01.00.00.01.00.00.00.00.0

Last rows

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13724137250.03.00000033.032.504101139.71617145.2878070.01.00.02.01.02.00.02.02.02.00.00.02.02.00.01.00.02.01.02.00.02.02.02.00.00.02.01.00.01.01.01.01.05.00.00.00.010.0000000.00.00.02.04.04.01.02.00.00.00.00.00.00.01.01.03.00.01.01.00.00.00.00.00.0000040.0000070.00.00.00.00.01.01.01.00.00.01.00.0
13725137260.01.00000032.032.327232139.725433101.0000000.01.01.01.01.03.01.02.01.01.01.01.02.03.01.01.01.01.01.02.01.02.03.01.02.01.01.02.01.01.01.01.01.04.00.00.00.010.0000000.00.00.04.05.02.01.01.00.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.07.00000011.0000000.03.02.00.01.01.00.01.00.00.01.00.0
13726137270.00.00002518.059.287609174.36517045.4976650.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.01.01.01.03.00.00.00.03.0001090.00.00.01.02.00.00.00.00.00.01.01.01.01.00.01.00.00.01.01.00.01.00.00.00.0000040.0000070.00.00.00.00.01.00.01.02.02.00.00.0
13727137280.00.00000022.032.816113139.77958745.2421070.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.00.04.00.00.00.012.0000000.00.00.03.04.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0000040.0000070.00.00.00.00.00.01.01.02.01.01.00.0
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13730137310.06.00000029.032.708584139.54481545.1100690.01.00.01.01.02.01.01.02.03.01.01.03.03.01.01.00.01.01.02.01.01.02.02.01.01.03.02.00.01.01.01.01.05.00.01.00.09.0000000.00.00.01.01.04.04.02.00.00.00.00.00.00.00.00.03.00.01.01.01.00.00.00.00.0000040.0000070.00.00.00.00.00.00.02.00.00.01.00.0
13731137320.021.00000037.058.075832169.235565108.0000000.01.01.02.02.02.00.00.01.02.01.01.03.03.01.02.01.02.02.02.00.00.01.02.01.01.03.03.01.02.01.01.01.04.01.00.00.06.0000001.00.00.02.02.04.04.00.02.00.00.00.01.00.01.00.04.00.01.02.00.01.00.00.00.0000040.0000070.00.00.00.00.01.00.00.00.00.01.00.0
13732137330.010.00000030.032.848503139.581711103.0000000.01.01.02.00.02.01.00.02.02.01.01.01.03.01.01.00.02.00.01.01.00.01.01.02.01.00.02.00.01.01.01.01.05.01.01.01.08.0000001.00.01.03.04.03.02.01.00.00.00.00.00.01.01.01.04.00.00.00.01.00.00.00.00.0000000.0000000.00.00.01.00.00.00.01.00.02.01.00.0
13733137340.06.00000026.070.732913176.873882102.0000000.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.00.01.00.01.00.01.01.04.00.00.00.09.0000000.00.00.02.01.01.02.00.00.00.00.00.01.01.01.01.00.00.02.02.01.01.00.00.00.0000040.0000070.00.00.00.00.00.00.01.00.02.01.00.0